Natural variability can make different instances of the same face appear remarkably dissimilar. Such variability rarely affects familiar face recognition. However, small differences in appearance between encounters can have really detrimental effects on identifying instances of unfamiliar faces as the same person. In typical face processing research, within-person variability is experimentally controlled, in order to explore the influences of between-person variability in face processing directly. That is, face stimuli are constrained so that differences between individual faces are restricted to identity-specific information; shape and texture. To this end, it remains unclear whether such natural variability plays a part in normal face processing. In this thesis, a series of experiments explore whether experiencing natural variability is beneficial in normal face processing. Specifically, the experiments described within this thesis address whether there is a role of within-person variability in face learning, with various manipulations, and also whether it has a role in improving unfamiliar face matching. The results suggest that experiencing variability is important in face learning – specifically in developing stable face representations. It was also found to be beneficial in improving unfamiliar face matching. Additional manipulations, such as the presence of additional person information, did not show any additional benefit to face learning – unlike previous studies. I suggest that the differences between the results observed here and previous studies highlight differences in measures of familiarity, and the importance of considering what different measures tell us about face processing. I discuss these findings in relation to previous face learning studies, in addition to face perception methodologies overall. Put simply, I suggest that in order to understand face identification processes comprehensively, it is important to consider both between- and within-person variability.
|Publisher||University of Aberdeen|
|Source Sets||Ethos UK|
|Type||Electronic Thesis or Dissertation|
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